Iterative launches machine learning management tool

Iterative, the MLOps company dedicated to streamlining the workflow of data scientists and machine learning (ML) engineers, has launched machine learning engineering management (MLEM) - an open source model deployment and registry tool that uses an organisation's existing Git infrastructure and workflows.

According to the company, MLEM is designed to bridge the gap between ML engineers and DevOps teams. DevOps teams can understand the underlying frameworks and libraries a model uses and automate deployment into a one-step process for production services and apps, Iterative states.

IDC AI/ML Lifecycle Management Softwrae research director Sriram Subramanian says, "Iterative enables customers to treat AI models as just another type of software artifact. The ability to build ML model registries using Git infrastructure and DevOps principles allows models to get into production faster.

MLEM is a core building block for a Git-based ML model registry, together with other Iterative tools, like GTO and DVC. A model registry stores and versions trained ML models. Model registries greatly simplify the task of tracking models as they move through the ML lifecycle, from training to production deployments and ultimately retirement.

Iterative co-founder and CEO Dmitry Petrov says, "Model registries simplify tracking models moving through the ML lifecycle by storing and versioning trained models, but organisations building these registries end up with two different tech stacks for machine learning models and software development.

"MLEM as a building block for model registries uses Git and traditional CI/CD tools, aligning ML and software teams so they can get models into production faster.

With Iterative tools, organisations can build a ML model registry based on software development tools and best practices, the company states. This means Git acts as a central source of truth for models, eliminating the need for external tools specific to machine learning.

All information around a model including which are in production, development, or deprecated, can all be viewed in Git. MLEM's modular nature fits into any organisation's software development workflows based on Git and CI/CD, without engineers having to transition to a separate machine learning deployment and registry tool. This allows teams to use a similar process across both ML models and applications for deployment, eliminating duplication in processes and code.

Teams are then able build a model registry in hours rather than days. MLEM promotes a comprehensive machine learning model lifecycle management workflow using a GitOps-based approach. Software development and MLOps teams can then be aligned, using the same tools to speed the time it takes a model to get from development to production.

Iterative was founded in 2018. Since that time, its tools have had more than 8 million sessions and are rapidly growing, with more than 12,000 stars on GitHub between CML and DVC. DVC users grew by almost 95% in 2021 with over 3000 monthly users. Iterative now has more than 300 contributors across the different tools.